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               2009年4月29日 下午9:40
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              <p>
               原帖
               <a class="d4pbbc-url" href="http://cos.name/cn/topic/15082.html" rel="nofollow" target="_blank">
                http://cos.name/cn/topic/15082
               </a>
              </p>
              <p>
               在上面的帖子里，sociology提到了可以让R的代数运算更快的方法：使用ATLAS。
               <br/>
               我之前在CRAN的contrib目录里也见到过，并在自己原来的P4机上尝试过，貌似失败了[s:12] ？反正当时没怎么注意。昨天看到那帖子之后就顺便测试了一下，然后被震惊了……
               <br/>
               然后就有了下文。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                一、ATLAS简介
               </strong>
               <br/>
               ATLAS=Automatically Tuned Linear Algebra Software，算是BLAS (Basic Linear Algebra Subprograms)的进阶版，是一种跨平台的线性代数软件（”ATLAS’s purpose is to provide portably optimal linear algebra software.”），目前在ATLAS的官方网站（
               <a class="d4pbbc-url" href="http://math-atlas.sourceforge.net" rel="nofollow" target="_blank">
               </a>
               http://math-atlas.sourceforge.net/[/url]）上给出的最新版本是3.9.11。根据官网上的数据，由于ATLAS针对不同的CPU及其支持的指令集进行了优化，故可以在进行代数运算时获得相对更快的速度（数据对比参见
               <a class="d4pbbc-url" href="http://math-atlas.sourceforge.net/timing" rel="nofollow" target="_blank">
               </a>
               http://math-atlas.sourceforge.net/timing/[/url]）。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                二、在R中使用ATLAS
               </strong>
               <br/>
               由于R中也使用了BLAS代数运算系统，所以也可以使用ATLAS提供的API来加速。具体是通过加载一个单独编译的dll文件（Rblas.dll）来实现ATLAS的应用。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                1.首先要知道你的电脑所使用的CPU类型。
               </strong>
               <br/>
               （R的ATLAS支持貌似只对Windows版本有效？我不熟悉Linux，不敢妄言，所以下文仅针对Windows平台）
               <br/>
               Windows XP：我的电脑——右键菜单——属性——常规选项卡，如下。
               <br/>
               <img src="http://www.bobd.cn/itschool/UploadFiles_6712/200612/2006122065336361.jpg"/>
               <br/>
               这台电脑使用的是Pentium 4的CPU
              </p>
              <p>
               WIndows VIsta/Windows 7：计算机——右键菜单——属性，在“系统”里的“处理器”可以看到
               <br/>
               <img src="http://img5.pcpop.com/ArticleImages/500×375/0/713/000713788.jpg"/>
               <br/>
               这台电脑使用的是Core2 Duo的CPU
              </p>
              <p>
               <strong class="d4pbbc-bold">
                2.选择对应你的CPU的版本下载对应的dll
               </strong>
               <br/>
               下载地址：
               <a class="d4pbbc-url" href="http://mirrors.geoexpat.com/cran/bin/windows/contrib/ATLAS" rel="nofollow" target="_blank">
               </a>
               http://mirrors.geoexpat.com/cran/bin/windows/contrib/ATLAS/[/url]
               <br/>
               比如，我是Athlon64 X2 5000+，下载的版本就是Athlon64_SSE3/ 文件夹下的
              </p>
              <p>
               注意，正在使用Core2 Duo的同学可能会失望一下……
              </p>
              <pre class="highlight ">This directory contains versions of Rblas.dll linked against the ATLAS library: 
- Athlon64_SSE3
- AthlonXP
- PII, PIII, P4, and PM (Pentium M) for various Pentiums
- We are looking for a volunteer providing a Core2Duo version. 
The Rblas.dll in PII, PIII and AthlonXP are linked against ATLAS 3.4.1,
P4 and PM against ATLAS 3.6.0,
and Athlon64_SSE3_512k against ATLAS 3.7.37. 
Rename ...\bin\Rblas.dll and drop the new Rblas.dll into the ...\bin folder. 
Using the wrong version will either not work at all or work much slower
than is possible.
</pre>
              <p>
               但事实上我测试的结果是无所谓，把P4的dll放我的Athlon64 x2上跑的也很欢……[s:12]
              </p>
              <p>
               <strong class="d4pbbc-bold">
                3.把下载好的dll文件复制到R的安装目录\bin\里面
               </strong>
               <br/>
               比如我的是放在C:\Program Files\R\R-2.9.0\bin里面，会提示覆盖（之前请关掉正在运行的R）
               <br/>
               这样你的R就拥有了ATLAS的线性代数运算系统[s:11]
               <br/>
               PS：升级到2.12.0版本后，位置变为：C:\Program Files\R\R-2.12.0\bin\i386里面
              </p>
              <p>
               <strong class="d4pbbc-bold">
                三、性能提升测试
                <br/>
               </strong>
               <br/>
               <strong class="d4pbbc-bold">
                测试环境说明：
               </strong>
               <br/>
               OS：WIndows 7 RC 7100
               <br/>
               CPU：AMD Athlon(tm) 64 X2 Dual Core Processor 5000+ 2.6G Hz
               <br/>
               内存：DDR II 800 2G X2
               <br/>
               R：2.9.0
               <br/>
               ATLAS补丁：Athlon64_SSE3_512k， ATLAS 3.7.37
              </p>
              <p>
               <strong class="d4pbbc-bold">
                1.矩阵代数运算
               </strong>
               <br/>
               因为ATLAS主打代数运算，我也首先进行一下矩阵代数运算的对比。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                1.1 矩阵外积运算
               </strong>
               （参见
               <a class="d4pbbc-url" href="http://cos.name/cn/topic/15082.html" rel="nofollow" target="_blank">
                http://cos.name/cn/topic/15082
               </a>
               ）
              </p>
              <pre class="highlight ">m=1000
n=2000
a=matrix(runif(m*n),m,n)
system.time(B&lt;-crossprod(a))
</pre>
              <p>
              </p>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]6.59 [/td][td=1,1,72]0.04 [/td][td=1,1,72]9.93 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]1.07 [/td][td=1,1,72]0.05 [/td][td=1,1,72]1.44 [/td][/tr][/table]
              </p>
              <p>
               <strong class="d4pbbc-bold">
                1.2 矩阵求逆运算
                <br/>
               </strong>
              </p>
              <pre class="highlight ">mat=round(abs(matrix(rnorm(2000*2000)*100,2000,2000)),0)
system.time(solve(mat))
</pre>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]28.75 [/td][td=1,1,72]0.08 [/td][td=1,1,72]29.16 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]6.26 [/td][td=1,1,72]0.10 [/td][td=1,1,72]6.44 [/td][/tr][/table]由此看出，ATLAS对R的线代运算的提升那是相当的明显啊……我就不做统计检验了，大家“眼球检验”一下好了[s:11] （我这是赝品，真正的Eyeball Test参见谢师兄
               <a class="d4pbbc-url" href="http://www.yihui.name/cn/post/175.htm" rel="nofollow" target="_blank">
                http://www.yihui.name/cn/post/175.htm
               </a>
               ）
              </p>
              <p>
               <strong class="d4pbbc-bold">
                2.循环运算
               </strong>
               <br/>
               用R不可能只做上面这些运算，编程时免不了循环，那么ATLAS是否会有提升呢。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                2.1 空循环
               </strong>
              </p>
              <pre class="highlight ">f=function(n)
{
for(i in 1:10^n){}
}
system.time(f(8))
</pre>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]26.90 [/td][td=1,1,72]0.24 [/td][td=1,1,72]27.58 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]27.11 [/td][td=1,1,72]0.09 [/td][td=1,1,72]27.76 [/td][/tr][/table]
              </p>
              <p>
               <strong class="d4pbbc-bold">
                2.2 雅可比法矩阵求逆
               </strong>
              </p>
              <pre class="highlight ">a=matrix(rep(0,30*30),30,30) 
for (i in 1:30)
{
for (j in 1:30)
{
if (i==j)a[i,j]=i+j
if (i!=j)a[i,j]=i*j
}
} 
error=1e-4 
c=NULL
v=NULL
r=NULL
t=0
k=0
d=a
v=diag(rep(1,30))
e=Inf
maxrl=function(x)
{
X=abs(x)
diag(X)=0
rl=which(X==max(X), arr.ind = TRUE)[1,]
return(rl)
} 
f=function()
{
while (e&gt;error)
{
k=k+1
rl=maxrl(d)
p=rl[1]
q=rl[2]
if (d[p,p]==d[q,q])
{
t=1
}
else
{
T=2*d[p,q]/(d[p,p]-d[q,q])
t=(-1+sqrt(1+T^2))/T
}
cos=1/sqrt(1+t^2)
sin=cos*t 
r=diag(rep(1,30))
r[p,p]=cos
r[p,q]=-sin
r[q,p]=sin
r[q,q]=cos 
d=t(r)%*%d%*%r
X=abs(d)
diag(X)=0
e=max(X)
v=v%*%r
}
}
system.time(f())
</pre>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]0.72 [/td][td=1,1,72]0.02 [/td][td=1,1,72]0.99 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]0.69 [/td][td=1,1,72]0.00 [/td][td=1,1,72]0.98 [/td][/tr][/table]可以看出，空循环下前后速度没有变化，雅可比求逆中虽也有矩阵运算，但计算量小，仍以循环为主，前后速度也没有变化。
              </p>
              <p>
               <strong class="d4pbbc-bold">
                3.数据挖掘算法
               </strong>
               <br/>
               有了上面的结果，我对这个也没什么太大期望了
              </p>
              <p>
               <strong class="d4pbbc-bold">
                3.1 关联规则分析
               </strong>
               <br/>
               Adult收入数据
              </p>
              <pre class="highlight ">system.time(rules &lt;- apriori(Adult, parameter = list(support = 0.01,confidence = 0.6)))
</pre>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]1.28 [/td][td=1,1,72]0.00 [/td][td=1,1,72]1.28 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]1.29 [/td][td=1,1,72]0.00 [/td][td=1,1,72]1.29 [/td][/tr][/table]
               <br/>
               <strong class="d4pbbc-bold">
                3.2 随机森林
                <br/>
               </strong>
               Adult收入数据，前2000条
              </p>
              <pre class="highlight ">system.time(randomForest(收入~.,data.part,mtry=3,importance=TRUE,proximity=TRUE))
</pre>
              <p>
               [table=288][tr][td][/td][td=1,1,72]用户[/td][td=1,1,72]系统[/td][td=1,1,72]流逝[/td][/tr][tr][td=1,1,72]替换前[/td][td=1,1,72]29.77 [/td][td=1,1,72]0.11 [/td][td=1,1,72]30.67 [/td][/tr][tr][td=1,1,72]替换后[/td][td=1,1,72]29.82 [/td][td=1,1,72]0.11 [/td][td=1,1,72]30.75 [/td][/tr][/table]果然……
              </p>
              <p>
               <strong class="d4pbbc-bold">
                四、总结
               </strong>
               <br/>
               ATLAS对大规模的代数运算有极其明显的提速效果，但是对别的运算无效，甚至会稍微减速。
               <br/>
               这从ATLAS的设计初衷就应该能看出来，只是我不死心，想侥幸试一试……结果挺令我挺失望的……所以大家酌情使用吧
              </p>
              <p>
               <strong class="d4pbbc-bold">
                附：REvolution R Version 1.3.0
               </strong>
               <br/>
               （下载：
               <a class="d4pbbc-url" href="http://cdn.cloudfiles.mosso.com/c28462/Revo-1.3.0-windows-32-installer.exe" rel="nofollow" target="_blank">
                Windows 32-bit Installer
               </a>
               (61.8 MB)）
               <br/>
               之前cloud_wei在这里
               <a class="d4pbbc-url" href="http://cos.name/bbs/read.php?tid=14919" rel="nofollow" target="_blank">
                http://cos.name/bbs/read.php?tid=14919
               </a>
               介绍过，蓝枫在
               <a class="d4pbbc-url" href="http://cos.name/cn/topic/15082.html" rel="nofollow" target="_blank">
                http://cos.name/cn/topic/15082
               </a>
               也进行了测试。我也试了一下，对上面所有的测试跑了一遍，REvolution R在矩阵代数运算中成绩最好，甚至可以略优于ATLAS，但在剩下的测试中没有任何提升，跟原生R 2.9.0基本持平甚至略有下降。而REvolution R在官网上的测评则看上去很吓人（
               <a class="d4pbbc-url" href="http://www.revolution-computing.com/products/r-performance.php?elq=E51F0BFBD9914AF187C333BB34A5E67B" rel="nofollow" target="_blank">
                http://www.revolution-computing.com/products/r-performance.php?elq=E51F0BFBD9914AF187C333BB34A5E67B
               </a>
               ），所以……貌似在吹牛？[s:12]
              </p>
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            <div class="bbp-reply-header" id="post-269360">
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              <span class="bbp-reply-post-date">
               2009年4月30日 上午6:11
              </span>
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               普通会员
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              <p>
               呵呵，linux版本怎么可能没有呢……搜了一下，这个应该可以用
               <br/>
               <a class="d4pbbc-url" href="http://linux.softpedia.com/get/Science-and-Engineering/Mathematics/ATLAS-4295.shtml" rel="nofollow" target="_blank">
                http://linux.softpedia.com/get/Science-and-Engineering/Mathematics/ATLAS-4295.shtml
               </a>
              </p>
              <p>
               thx for sharing
              </p>
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            <div class="bbp-reply-header" id="post-269362">
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               2009年4月30日 上午6:30
              </span>
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               3 楼
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              <p>
               顶
               <br/>
               测试结果让我对Revolution R有点小失望……
               <br/>
               不过如果是多线程呢
              </p>
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            <div class="bbp-reply-header" id="post-269363">
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               2009年4月30日 上午6:41
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               4 楼
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              <p>
               R跑不了多线程吧
              </p>
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            <div class="bbp-reply-header" id="post-269364">
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              <span class="bbp-reply-post-date">
               2009年4月30日 上午6:47
              </span>
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               5 楼
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              <p>
               嗯，官网上也给出了sourceforge.net的下载地址。
               <br/>
               只不过大致看了一下，那好像是ATLAS的源码？Linux的R下不知道能不能直接调用了……
               <br/>
               Win下加载dll倒是挺方便
               <img src="http://rs.phpwind.net/E___4239ZHYXPWFG.gif"/>
              </p>
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            <div class="bbp-reply-header" id="post-269367">
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              <span class="bbp-reply-post-date">
               2009年4月30日 上午6:56
              </span>
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               6 楼
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              <p>
               R是单线程的。做大的矩阵运算还是愿意用MATLAB，总觉得R的速度差的不是一点半点
              </p>
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               2009年4月30日 上午7:07
              </span>
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               7 楼
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              <p>
               呵呵，有时是有这种感觉。
               <br/>
               刚去邮件列表看了看，找到篇比较搞的……回复毫不给人面子啊
               <br/>
               <a class="d4pbbc-url" href="http://finzi.psych.upenn.edu/R/Rhelp08/archive/145131.html" rel="nofollow" target="_blank">
                http://finzi.psych.upenn.edu/R/Rhelp08/archive/145131.html
               </a>
              </p>
              <p>
               里面说的简而言之就是
               <br/>
               1.R本身是单任务单线程，不能跑在多核上
               <br/>
               2.R中部分功能，比如BLAS是多线程的，但对于大部分R的任务没什么用处，除非有大量代数运算
               <br/>
               3.使用一些包（比如 snow和 Rmpi），同时拆分重写代码，可以变相实现多核并行
              </p>
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               2009年4月30日 上午7:08
              </span>
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              <p>
               Revolution R可以吧，看这个
               <br/>
               <a class="d4pbbc-url" href="http://www.revolution-computing.com/products/benchmarks.php" rel="nofollow" target="_blank">
                http://www.revolution-computing.com/products/benchmarks.php
               </a>
              </p>
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               2009年4月30日 上午7:41
              </span>
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               9 楼
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              <p>
               已测。
               <br/>
               确实如其所言
              </p>
              <pre class="highlight "> 
set.seed (1) 
m &lt;- 10000 
n &lt;-  5000 
A &lt;- matrix (runif (m*n),m,n) 
system.time (B &lt;- crossprod(A)) 
</pre>
              <p>
               就以这个为例，Revolution R的确可以让两个core跑满100%，速度提升不少。
               <br/>
               但是很可能仅对某些特定的矩阵运算函数有效，我跑arule包时依然是50%，甚至一些最基本的循环跑的时候也只有50%。
              </p>
              <p>
               ——这样的话这个所谓的多核多线程意义不大……谁没事整天跑矩阵相乘啊[s:11]
              </p>
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            <div class="bbp-reply-header" id="post-269471">
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              <span class="bbp-reply-post-date">
               2009年4月30日 下午2:17
              </span>
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               10 楼
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              <p>
               SPRINT: A new parallel framework for R
               <br/>
               <a class="d4pbbc-url" href="http://view.ncbi.nlm.nih.gov/pubmed/19114001" rel="nofollow" target="_blank">
                http://view.ncbi.nlm.nih.gov/pubmed/19114001
               </a>
               <br/>
               <a class="d4pbbc-url" href="http://forge.nesc.ac.uk/projects/sprint/" rel="nofollow" target="_blank">
                http://forge.nesc.ac.uk/projects/sprint/
               </a>
               <br/>
               <strong class="d4pbbc-bold">
                BACKGROUND:
               </strong>
               <br/>
               Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest.
               <br/>
               <strong class="d4pbbc-bold">
                RESULTS:
                <br/>
               </strong>
               We have designed and built a prototype framework that allows the addition of parallelised functions to R to enable the easy exploitation of HPC systems. The Simple Parallel R INTerface (SPRINT) is a wrapper around such parallelised functions. Their use requires very little modification to existing sequential R scripts and no expertise in parallel computing. As an example we created a function that carries out the computation of a pairwise calculated correlation matrix. This performs well with SPRINT. When executed using SPRINT on an HPC resource of eight processors this computation reduces by more than three times the time R takes to complete it on one processor.
               <br/>
               <strong class="d4pbbc-bold">
                CONCLUSIONS:
               </strong>
               <br/>
               SPRINT allows the biostatistician to concentrate on the research problems rather than the computation, while still allowing exploitation of HPC systems. It is easy to use and with further development will become more useful as more functions are added to the framework.
              </p>
              <p>
               R/parallel – speeding up bioinformatics analysis with R
               <br/>
               <a class="d4pbbc-url" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&amp;pubmedid=18808714" rel="nofollow" target="_blank">
                http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&amp;pubmedid=18808714
               </a>
               <br/>
               <a class="d4pbbc-url" href="http://www.rparallel.org" rel="nofollow" target="_blank">
                http://www.rparallel.org
               </a>
               <br/>
               <strong class="d4pbbc-bold">
                BACKGROUND:
                <br/>
               </strong>
               R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.
               <br/>
               <strong class="d4pbbc-bold">
                RESULTS:
               </strong>
               <br/>
               We have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of today's desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores.
               <br/>
               <strong class="d4pbbc-bold">
                CONCLUSION:
               </strong>
               <br/>
               R/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.
              </p>
              <p>
               没有测试过，大家可以参考一下。
              </p>
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            <div class="bbp-reply-header" id="post-269491">
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               2009年4月30日 下午4:18
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               未经同意就给你稍微排了下版，不好意思哈^希望表生气
              </p>
              <p>
               给biostatisticians用的啊……啥时候跑个简单的for也能用上multi-core就好了 [s:12]
              </p>
              <p>
               R的并行处理始终是个怨念，不知道R 3.0.0会不会来个惊喜呢 [s:11]
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               2009年4月30日 下午5:46
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               关于R的速度问题，的确比较麻烦，每次做loop的时候都把我的破本子放在一边跑，自己去做其他的事。
               <br/>
               个人的选择是combine python and R,
               <br/>
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               然后大数据时只是调用R 里面的library
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               2009年5月1日 上午5:44
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               哈哈，挺好的~
               <br/>
               整合才是王道
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               2009年8月7日 下午5:19
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               mark
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               2009年10月28日 上午5:51
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              <p>
               看看这个：
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